90 research outputs found

    Models and Analysis of Vocal Emissions for Biomedical Applications

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    The Models and Analysis of Vocal Emissions with Biomedical Applications (MAVEBA) workshop came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

    Sequential grouping constraints on across-channel auditory processing

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    Tailoring structures using stochastic variations of structural parameters.

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    Imperfections, meaning deviations from an idealized structure, can manifest through unintended variations in a structure’s geometry or material properties. Such imperfections affect the stiffness properties and can change the way structures behave under load. The magnitude of these effects determines how reliable and robust a structure is under loading. Minor changes in geometry and material properties can also be added intentionally, creating a more beneficial load response or making a more robust structure. Examples of this are variable stiffness composites, which have varying fiber paths, or structures with thickened patches. The work presented in this thesis aims to introduce a general approach to creating geodesic random fields in finite elements and exploiting these to improve designs. Random fields can be assigned to a material or geometric parameter. Stochastic analysis can then quantify the effects of variations on a structure for a given type of imperfection. Information extracted from the effects of imperfections can also identify areas critical to a structure’s performance. Post-processing stochastic results by computing the correlation between local changes and the structural performance result in a pattern, describing the effects of local changes. Perturbing the ideal deterministic geometry or material distribution of a structure using the pattern of local influences can increase performance. Examples demonstrate the approach by increasing the deterministic (without imperfections applied) linear buckling load, fatigue life, and post-buckling path of structures. Deterministic improvements can have a detrimental effect on the robustness of a structure. Increasing the amplitude of perturbation applied to the original design can improve the robustness of a structure’s response. Robustness analyses on a curved composite panel show that increasing the amplitude of design changes makes a structure less sensitive to variations. The example studied shows that an increase in robustness comes with a relatively small decrease in the deterministic improvement.Imperfektionen, d. h. die Abweichungen von einer idealisierten Struktur, können sich durch unbeabsichtigte Variationen in der Geometrie oder den Materialeigenschaften einer Struktur ergeben. Solche Imperfektionen wirken sich auf die Steifigkeitseigenschaften aus und können das Verhalten von Strukturen unter Last verändern. Das Ausmaß dieser Auswirkungen bestimmt, wie zuverlässig und robust eine Struktur unter Belastung ist. Kleine Änderungen der Geometrie und der Materialeigenschaften können auch absichtlich eingebaut werden, um ein verbessertes Lastverhalten zu erreichen oder eine stabilere Struktur zu schaffen. Beispiele hierfür sind Verbundwerkstoffe mit variabler Steifigkeit, die unterschiedliche Faserverläufe aufweisen, oder Strukturen mit lokalen Verstärkungen. Die in dieser Dissertation vorgestellte Arbeit zielt darauf ab, einen allgemeinen Ansatz zur Erstellung geodätischer Zufallsfelder in Finiten Elementen zu entwickeln und diese zur Verbesserung von Konstruktionen zu nutzen. Zufallsfelder können Material- oder Geometrieparametern zugeordnet werden. Die stochastische Analyse kann dann die Auswirkungen von Variationen auf eine Struktur für eine bestimmte Art von Imperfektion quantifizieren. Die aus den Auswirkungen von Imperfektionen gewonnenen Informationen können auch Bereiche identifizieren, die für das Tragvermögen einer Struktur kritisch sind. Die Auswertung der stochastischen Ergebnisse durch Berechnung der Korrelation zwischen lokalen Veränderungen und Strukturtragvermögen ergibt ein Muster, das die Auswirkungen lokaler Veränderungen beschreibt. Die Perturbation der idealen deterministischen Geometrie oder der Materialverteilung einer Struktur unter Verwendung des Musters der lokalen Einflüsse kann das Tragvermögen erhöhen. Anhand von Beispielen wird der Ansatz durch die Erhöhung der deterministischen (ohne Imperfektionen) linearen Knicklast, der Lebensdauer und des Nachknickverhaltens von Strukturen aufgezeigt. Deterministische Verbesserungen können sich zum Nachteil der Robustheit einer Struktur auswirken. Eine Vergrößerung der Amplitude der auf den ursprünglichen Designentwurf angewendeten Perturbation kann die Robustheit der Reaktion einer Struktur verbessern. Robustheitsanalysen an einer gekrümmten Verbundplatte zeigen, dass eine Struktur durch eine Vergrößerung der Amplitude der Entwurfsänderungen weniger empfindlich gegenüber Abweichungen wird. Das untersuchte Beispiel zeigt, dass eine Erhöhung der Robustheit mit einem relativ geringen Verlust der deterministischen Verbesserung eingeht

    State-of-the-Art Sensors Technology in Spain 2015: Volume 1

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    This book provides a comprehensive overview of state-of-the-art sensors technology in specific leading areas. Industrial researchers, engineers and professionals can find information on the most advanced technologies and developments, together with data processing. Further research covers specific devices and technologies that capture and distribute data to be processed by applying dedicated techniques or procedures, which is where sensors play the most important role. The book provides insights and solutions for different problems covering a broad spectrum of possibilities, thanks to a set of applications and solutions based on sensory technologies. Topics include: • Signal analysis for spectral power • 3D precise measurements • Electromagnetic propagation • Drugs detection • e-health environments based on social sensor networks • Robots in wireless environments, navigation, teleoperation, object grasping, demining • Wireless sensor networks • Industrial IoT • Insights in smart cities • Voice recognition • FPGA interfaces • Flight mill device for measurements on insects • Optical systems: UV, LEDs, lasers, fiber optics • Machine vision • Power dissipation • Liquid level in fuel tanks • Parabolic solar tracker • Force sensors • Control for a twin roto

    Evolutionary Algorithms in Engineering Design Optimization

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    Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc

    Optimization of habitat suitability models for freshwater species distribution using evolutionary algorithms

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